In this paper we present a novel approach for counting
geometrical objects and finding interesting points of geometrical objects within
an image. Primary attention is given to a certain class of images that contain
feasibly visible objects at a considerable scale. The geometric shapes discussed
here are initially limited to squares, rectangles, circles, triangles. Inputs are also taken
from photographs where geometrical objects are present. Since the segmentation
algorithms by means of edge detection [3] or any similar method provide highly
accurate results only for gray level images, all the inputs are converted into
gray images before subsequent processing. The results are very encouraging and
show the potential usage of this approach in various applications of robotics,
geography, statistics etc, where we actually do not require recognizing the
object by its content but by the percentage of area of the objects in the whole
image. The future scope could be potentially extended to polygons, ovals and
curves. Furthermore, the execution speed of our approach could be improved by adoption
of convenient parallel execution architecture and programming framework, i.e.
CUDA (Compute
Unified Device Architecture) on GPU chip from NVIDIA.